- inverse.predict now has a var.s argument instead of the never

  tested ss argument. This is documented in the updated vignette
- loq() now has w.loq and var.loq arguments, and stops with a message
  if neither are specified and the model has weights.
- calplot doesn't stop any more for weighted regression models, but
  only refrains from drawing prediction bands
- Added method = "din" to lod(), now that I actually have it (DIN 32645) and
  was able to see which approximation is used therein.
- removed the demos, as the examples and tests are already partially 
  duplicated
- The vignette is more of a collection of various notes, but should 
  certainly be helpful for the user.
- Version bump to 0.1-xxx



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@16 5fad18fb-23f0-0310-ab10-e59a3bee62b4

1 files changed, 37 insertions, 10 deletions

diff --git a/tests/massart97.Rout.save b/tests/massart97.Rout.save
index ae50275..9386a11 100644
--- a/tests/massart97.Rout.save
+++ b/tests/massart97.Rout.save
@@ -1,6 +1,6 @@
 
 R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.0 (2006-04-24)
+Version 2.3.1 (2006-06-01)
 ISBN 3-900051-07-0
 
 R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -15,17 +15,20 @@ Type 'demo()' for some demos, 'help()' for on-line help, or
 'help.start()' for an HTML browser interface to help.
 Type 'q()' to quit R.
 
-> library(chemCal)
+> require(chemCal)
+Loading required package: chemCal
+[1] TRUE
 > data(massart97ex3)
 > attach(massart97ex3)
-> yx <- split(y,x)
-> ybar <- sapply(yx,mean)
-> s <- round(sapply(yx,sd),digits=2)
-> w <- round(1/(s^2),digits=3)
+> yx <- split(y, x)
+> ybar <- sapply(yx, mean)
+> s <- round(sapply(yx, sd), digits = 2)
+> w <- round(1 / (s^2), digits = 3)
 > weights <- w[factor(x)]
-> m <- lm(y ~ x,w=weights)
-> # The following concords with the book
-> inverse.predict(m, 15, ws = 1.67)
+> m <- lm(y ~ x, w = weights)
+> #calplot(m)
+> 
+> inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
 $Prediction
 [1] 5.865367
 
@@ -38,7 +41,7 @@ $Confidence
 $`Confidence Limits`
 [1] 3.387082 8.343652
 
-> inverse.predict(m, 90, ws = 0.145)
+> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
 $Prediction
 [1] 44.06025
 
@@ -52,3 +55,27 @@ $`Confidence Limits`
 [1] 36.20523 51.91526
 
 > 
+> m0 <- lm(y ~ x) 
+> lod(m0)
+$x
+[1] 5.406637
+
+$y
+[1] 13.63822
+
+> 
+> loq(m0)
+$x
+[1] 13.97767
+
+$y
+[1] 30.62355
+
+> loq(m, w.loq = 1.67)
+$x
+[1] 7.346231
+
+$y
+[1] 17.90784
+
+>